Less overhead.
More headspace.

A tangled cotton thread unwinds into a single loose strand on a blue surface

Keep your router.
Keep your provider.
Untangle the cost.

Good company.
Better combinations.

Already have a model provider or LLM router you like? Bring it. String looks for a better way to run your product’s AI features around the choices you want to keep.

OpenAI
Azure
Vertex AI
Jev by

AI cost optimization for production

Make your AI features less expensive to run.

String helps lower the cost of the AI features running inside your product. It studies your code and real requests, then tests changes against the quality and speed those features need to deliver.

From prompts and model choices to caching, batching and routing, String evaluates how the whole feature could run more efficiently. Keep the providers and tools you like. You decide what changes.

Start with what’s working.

Keep the tools and providers you like. String uses your code, traces and requirements to establish a baseline and find places worth investigating.

Look beyond the model swap.

Prompts, caching, batching and workflow design can change the economics too. String helps evaluate the combination, not just the price per token.

New possibilities. Same roadmap.

String helps you evaluate technologies like Jev, find where they fit, and uncover savings without pulling your team away from its roadmap.

You choose what ships.

Review the evidence and choose the changes worth making. Your team stays in control of implementation and release timing.

A router picks the route.
There’s more to the trip.

Your router
or model provider

Where should
this request go?

Model and provider selection.
Routing policies. Fallbacks.

String

How could this whole feature cost less?

App-specific evaluations.
Prompts, models and implementation choices.

Better, together.

Let your router or model provider keep doing its job. Use String to evaluate the wider set of choices around it. You decide which changes are worth making.

90%

A million job descriptions.
A much smaller bill.

Using String in Alpha, ApplyAll.com reduced costs by 90% on an internal feature extracting data from and classifying a million job descriptions a month.

The optimization included testing 48 prompt–model combinations, batching requests for caching, and using Jev to route simple cases to cheaper models.

“Should we
switch models?”

Make it
String’s problem.

Another model launch. Another price cut. String is being built to keep testing ways to lower your AI bill in the background, so you can skip the headlines and keep building.

Less overhead.More headspace.

Good company.Better combinations.

Make your AI features less expensive to run.

A router picks the route.There’s more to the trip.

Your routeror model provider